Increasing the share of correct clustering of characteristic signal with random losses in self-organizing maps

Abstract

Analysis of methods for optimizing algorithms of functioning of the Kohonen neural networks, self-organizing maps (SOM), in terms of training speed and percentage of correct clustering was made. Effective optimization of self-organizing maps was determined by the second criterion, the enhanced self-organizing incremental neural network (ESOINN). It was established that in the case of incomplete input signal, that is the signal with losses at unknown time points, the share of correct clustering is unacceptably low with any SOM algorithms, both basic and optimized. The incomplete signal was represented as the input vector of the neural network, the values of which are represented by a single array, that is without taking into consideration conformity of the moments of losses to the current values and without the possibility of determining these moments. A method for determining conformance of the incomplete input vector to the input layer of neurons to increase percentage of correct recognition was programmed and proposed. The method is based on finding the minimum distance between the current input vector and the vector of weights of each neuron. To reduce operating time of the algorithm, it was proposed to operate not with individual values of the input signal but their indivisible parts and the corresponding groups of input neurons. The proposed method was implemented for the SOM and ESOINN. To prove effectiveness of implementation of the basic algorithm of the SOM, its comparison with existing counterparts of other developers was made. A mathematical model was developed for formation of examples of complete signals of a training sample on the basis of reference curves of the second order and a training sample was generated. In accordance with the training sample, training of all neural networks implemented with and without the proposed method was made. A diagram of simulation of losses was developed and test samples were generated for computational experiments with incomplete signals. On the basis of experiments, efficiency of the proposed method for classification in terms of incomplete input signal on the basis of self-organizing maps was proved both for implementations of the basic algorithm of SOM and ESOINN